clustering {Clustering}

Clustering algorithm.

Description

Discovering the behavior of attributes in a set of clustering packages based on evaluation metrics.

Usage

clustering(
  path = NULL,
  df = NULL,
  packages = NULL,
  algorithm = NULL,
  min = 3,
  max = 4,
  metrics = NULL
)

Arguments

path

The path of file. NULL It is only allowed to use path or df but not both at the same time. Only files in .dat, .csv or arff format are allowed.

df

data matrix or data frame, or dissimilarity matrix. NULL If you want to use training and test basketball attributes.

packages

character vector with the packets running the algorithm.

NULL The seven packages implemented are: cluster, ClusterR, amap, apcluster, pvclust.
By default runs all packages.

algorithm

character vector with the algorithms implemented within the package. NULL The algorithms implemented are: hclust,apclusterK,agnes,clara,daisy, diana,fanny,mona,pam,gmm, kmeans_arma,kmeans_rcpp,mini_kmeans,pvclust.

min

An integer with the minimum number of clusters This data is necessary to indicate the minimum number of clusters when grouping the data. The default value is 3.

max

An integer with the maximum number of clusters. This data is necessary to indicate the maximum number of clusters when grouping the data. The default value is 4.

metrics

Character vector with the metrics implemented to evaluate the distribution of the data in clusters. NULL The night metrics implemented are: Entropy, Variation_information,
Precision,Recall,F_measure,Fowlkes_mallows_index,Connectivity,Dunn and Silhouette.

Details

The operation of this algorithm is to evaluate how the attributes of a dataset or a set of datasets behave in different clustering algorithms. To do this, it is necessary to indicate the type of evaluation you want to make on the distribution of the data. To be able to execute the algorithm it is necessary to indicate the number of clusters.

min and max, the algorithms algorithm or packages.

packages that we want to cluster and the metrics metrics.

Value

A matrix with the result of running all the metrics of the algorithms contained in the packages indicated. We also obtain information with the types of metrics, algorithms and packages executed.

• result It is a list with the algorithms, metrics and variables defined in the execution of the algorithm.

• has_internal_metrics Boolean field to indicate if there are internal metrics such as: dunn, silhoutte and connectivity.

• has_external_metrics Boolean field to indicate if there are external metrics such as: precision, recall, f-measure, entropy, variation information and fowlkes-mallows.

• algorithms_execute Character vector with the algorithms executed. These algorithms have been mentioned in the definition of the parameters.

• measures_execute Character vector with the measures executed. These measures have been mentioned in the definition of the parameters.

Examples

Clustering::clustering(
     df = cluster::agriculture,
     min = 3,
     max = 3,
     algorithm='clara',
     metrics=c('Precision')
)